Plethysmograph pulse recognition processor

ABSTRACT

A time domain rule-based processor provides recognition of individual pulses in a pulse oximeter-derived photo-plethysmograph waveform.

REFERENCE TO RELATED APPLICATION

The present application claims priority benefit under 35 U.S.C. § 120to, and is a continuation of U.S. patent application Ser. No.10/267,446, filed Oct. 8, 2002 now U.S. Pat. No. 6,818,741, entitled“Plethysmograph Pulse Recognition Processor,” which is a continuation ofU.S. patent application Ser. No. 09/471,510, filed Dec. 23, 1999,entitled “Plethysmograph Pulse Recognition Processor,” now U.S. Pat. No.6,463,311, which claims priority benefit under 35 U.S.C. § 119(e) fromU.S. Provisional Application No. 60/114,127, filed Dec. 30, 1998,entitled “Plethysmograph Pulse Recognition Processor.” The presentapplication also incorporates the foregoing utility disclosure herein byreference.

BACKGROUND OF THE INVENTION

Oximetry is the measurement of the oxygen status of blood. Earlydetection of low blood oxygen is critical in the medical field, forexample in critical care and surgical applications, because aninsufficient supply of oxygen can result in brain damage and death in amatter of minutes. Pulse oximetry is a widely accepted noninvasiveprocedure for measuring the oxygen saturation level of arterial blood,an indicator of oxygen supply. A pulse oximeter typically provides anumerical readout of the patient's oxygen saturation, a numericalreadout of pulse rate, and an audible indicator or “beep” that occurs ateach pulse.

A pulse oximetry system consists of a sensor attached to a patient, amonitor, and a cable connecting the sensor and monitor. Conventionally,a pulse oximetry sensor has both red and infrared (IR) light-emittingdiode (LED) emitters and a photodiode detector. The sensor is typicallyattached to an adult patient's finger or an infant patient's foot. For afinger, the sensor is configured so that the emitters project lightthrough the fingernail and into the blood vessels and capillariesunderneath. The photodiode is positioned at the fingertip opposite thefingernail so as to detect the LED emitted light as it emerges from thefinger tissues.

The pulse oximetry monitor (pulse oximeter) determines oxygen saturationby computing the differential absorption by arterial blood of the twowavelengths emitted by the sensor. The pulse oximeter alternatelyactivates the sensor LED emitters and reads the resulting currentgenerated by the photodiode detector. This current is proportional tothe intensity of the detected light. The pulse oximeter calculates aratio of detected red and infrared intensities, and an arterial oxygensaturation value is empirically determined based on the ratio obtained.The pulse oximeter contains circuitry for controlling the sensor,processing the sensor signals and displaying the patient's oxygensaturation and pulse rate. In addition, a pulse oximeter may display thepatient's plethysmograph waveform, which is a visualization of bloodvolume change in the illuminated tissue caused by arterial blood flowover time. A pulse oximeter is described in U.S. Pat. No. 5,632,272assigned to the assignee of the present invention.

SUMMARY OF THE INVENTION

FIG. 1 illustrates the standard plethysmograph waveform 100, which canbe derived from a pulse oximeter. The waveform 100 is a display of bloodvolume, shown along the y-axis 110, over time, shown along the x-axis120. The shape of the plethysmograph waveform 100 is a function of heartstroke volume, pressure gradient, arterial elasticity and peripheralresistance. The ideal waveform 100 displays a broad peripheral flowcurve, with a short, steep inflow phase 130 followed by a 3 to 4 timeslonger outflow phase 140. The inflow phase 130 is the result of tissuedistention by the rapid blood volume inflow during ventricular systole.During the outflow phase 140, blood flow continues into the vascular bedduring diastole. The end diastolic baseline 150 indicates the minimumbasal tissue perfusion. During the outflow phase 140 is a dicrotic notch160, the nature of which is disputed. Classically, the dicrotic notch160 is attributed to closure of the aortic valve at the end ofventricular systole. However, it may also be the result of reflectionfrom the periphery of an initial, fast propagating, pressure pulse thatoccurs upon the opening of the aortic valve and that precedes thearterial flow wave. A double dicrotic notch can sometimes be observed,although its explanation is obscure, possibly the result of reflectionsreaching the sensor at different times.

FIG. 2 is a graph 200 illustrating a compartmental model of theabsorption of light at a tissue site illuminated by a pulse oximetrysensor. The graph 200 has a y-axis 210 representing the total amount oflight absorbed by the tissue site, with time shown along an x-axis 220.The total absorption is represented by layers, including the staticabsorption layers due to tissue 230, venous blood 240 and a baseline ofarterial blood 250. Also shown is a variable absorption layer due to thepulse-added volume of arterial blood 260. The profile 270 of thepulse-added arterial blood 260 is seen as the plethysmograph waveform100 depicted in FIG. 1.

FIG. 3 illustrates the photo-plethysmograph intensity signal 300detected by a pulse oximeter sensor. A pulse oximeter does not directlydetect absorption and, hence, does not directly measure the standardplethysmograph waveform 100 (FIG. 1). However, the standardplethysmograph can be derived by observing that the detected intensitysignal 300 is merely an out of phase version of the absorption profile270. That is, the peak detected intensity 372 occurs at minimumabsorption 272 (FIG. 2), and the minimum detected intensity 374 occursat maximum absorption 274 (FIG. 2). Further, a rapid rise in absorption276 (FIG. 2) during the inflow phase of the plethysmograph is reflectedin a rapid decline 376 in intensity, and the gradual decline 278 (FIG.2) in absorption during the outflow phase of the plethysmograph isreflected in a gradual increase 378 in detected intensity.

In addition to blood oxygen saturation, a desired pulse oximetryparameter is the rate at which the heart is beating, i.e. the pulserate. At first glance, it seems that it is an easy task to determinepulse rate from the red and infrared plethysmograph waveforms describedabove. However, this task is complicated, even under ideal conditions,by the variety of physiological plethysmographic waveforms. Further,plethysmographic waveforms are often corrupted by noise, includingmotion artifact, as described in U.S. Pat. No. 2,632,272 cited above.Plethysmograph pulse recognition, especially in the presence of motionartifact and other noise sources, is a useful component for determiningpulse rate and also for providing a visual or audible indication ofpulse occurrence.

In one aspect of the pulse recognition processor according to thepresent invention, information regarding pulses within an inputplethysmograph waveform is provided at a processor output. The processorhas a candidate pulse portion that determines a plurality of potentialpulses within the input waveform. A physiological model portion of theprocessor then determines the physiologically acceptable ones of thesepotential pulses. The processor may further provide statistics regardingthe acceptable pulses. One statistic is pulse density, which is theratio of the period of acceptable pulses to the duration of an inputwaveform segment.

The candidate pulse portion has a series of components that remove fromconsideration as potential pulses those waveform portions that do notcorrespond to an idealized triangular waveform. This processing removesirrelevant waveform features such as the characteristic dicrotic notchand those caused by noise or motion artifact. The candidate pulseportion provides an output having indices that identify potential pulsesrelative to the peaks and valleys of this triangular waveform.

The physiological model portion of the processor has a series ofcomponents that discard potential pulses that do not compare to aphysiologically acceptable pulse. The first component of the modelportion extracts features of the potential pulses, including pulsestarting point, pulse period, and pulse signal strength. These featuresare compared against various checks, including checks for pulses thathave a period below a predetermined threshold, that are asymmetric, thathave a descending trend that is generally slower that a subsequentascending trend, that do not sufficiently comply with an empiricalrelationship between pulse rate and pulse signal strength, and that havea signal strength that differs from a short-term average signal strengthby greater than a predetermined amount.

In another aspect of the present invention, a pulse recognition methodincludes the steps of identifying a plurality of potential pulses in aninput waveform and comparing the potential pulses to a physiologicalpulse model to derive at least one physiologically acceptable pulse. Afurther step of generating statistics for acceptable pulses may also beincluded. The generating step includes the steps of determining a totalperiod of acceptable pulses and calculating a ratio of this total periodto a duration of an input waveform segment to derive a pulse densityvalue. The comparing step includes the steps of extracting pulsefeatures from potential pulses and checking the extracted featuresagainst pulse criteria.

Yet another aspect of the current invention is a pulse recognitionprocessor having a candidate pulse means for identifying potentialpulses in an input waveform and providing a triangular waveform output.The processor also has a plethysmograph model means for determiningphysiologically acceptable pulses in the triangular waveform output andproviding as a pulse output the indices of acceptable pulses. The pulserecognition processor may further have a pulse statistics means fordetermining cumulative pulse characteristics from said pulse output.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be described in detail below in connectionwith the following drawing figures in which:

FIG. 1 is a graph illustrating a single pulse of a plethysmographwaveform;

FIG. 2 is a graph illustrating the absorption contribution of variousblood and tissue components;

FIG. 3 is a graph illustrating an intensity “plethysmograph” pulseoximetry waveform;

FIG. 4 is a block diagram of the plethysmograph pulse recognitionprocessor according to the present invention;

FIG. 5 is a block diagram of the candidate pulse finding subprocessorportion of the present invention;

FIG. 6 is a graph illustrating the filtered, curvature of aplethysmograph pulse and the associated edges;

FIG. 7 is a graph illustrating the delta T check on the edges;

FIG. 8 is a graph illustrating the zero-crossing check on the edges;

FIG. 9 is a graph illustrating the amplitude threshold check on theedges;

FIG. 10 is a graph illustrating the max-min check on the edges;

FIG. 11 is a graph illustrating the output of the pulse finder;

FIG. 12 is a block diagram of the plethysmograph model subprocessorportion of the present invention;

FIG. 13 is a graph illustrating the parameters extracted by the pulsefeatures component of the model subprocessor;

FIG. 14 is a graph illustrating the stick model check on the candidatepulses;

FIG. 15 is a graph illustrating an angle check on the candidate pulses;

FIG. 16 is a graph illustrating a pulse that would be discarded by theangle check;

FIG. 17 is a graph illustrating a pulse that would be discarded by theratio check;

FIG. 18 is a graph illustrating one test of the signal strength check;and

FIG. 19 is a block diagram of a pulse rate selection and comparisonmodule in accordance with a preferred embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 4 illustrates the plethysmograph pulse recognition processor 400according to the present invention. The pulse processor 400 has threesubprocessors, a candidate pulse subprocessor 410, a plethysmographmodel subprocessor 460, and a pulse statistics subprocessor 490. Thecandidate pulse subprocessor 410 applies various waveform criteria or“edge checks” to find candidate pulses in an input waveform “snapshot”412. In a particular embodiment, the snapshot is 400 samples of adetected intensity plethysmograph taken at a 62.5 Hz sampling rate. Thissnapshot represents a 6.4 second waveform segment. The output 414 of thecandidate pulse subprocessor 410 is peaks and valleys of the inputwaveform segment representing a triangular wave model of identifiedcandidate pulses. The candidate pulse output 414 is input to theplethysmograph model subprocessor 460, which compares these candidatepulses to an internal model for physiological pulses. The output 462 ofthe plethysmograph model subprocessor 460 is physiologically acceptablepulses. The acceptable pulse output 462 is input to the pulse statisticssubprocessor. The output 492 of the pulse statistics subprocessor isstatistics regarding acceptable pulses, including mean pulse period andpulse density, as described below.

FIG. 5 illustrates the components of the candidate pulse subprocessor410. This subprocessor removes waveform features that do not correspondto an idealized triangular waveform, including the characteristicdicrotic notch. For example, as shown in FIG. 3, the candidate pulsecomponent must identify points ABE, discarding points CD. The candidatepulse subprocessor 410 first identifies “edges” within the inputwaveform segment. An edge is defined as a segment that connects a peakand subsequent valley of the filtered waveform signal. The candidatepulse processor 410 then discards edges that do not meet certainconditions.

As shown in FIG. 5, the candidate pulse subprocessor has curvature 500,low-pass filter 510 (in one embodiment) and edge finder 520 componentsthat identify edges. In one embodiment, the curvature component 510 isimplemented by convolving the waveform with the kernel [1,−2,1]. In oneembodiment, instead of a low-pass filter 510, a band-pass filter can beused. For a kernel size of n, this can be represented as follows:y _(k) =wy _(k-1) +u _(k)  (1)where u_(k) is the kth input sample and y_(k) is the kth output sampleand w is a fixed weight that determines the amount of filter feedback.The edge finder 520 identifies the peaks and subsequent valleys of theoutput of the filter 510.

FIG. 6 illustrates the results of the curvature 500, filter 510 and edgefinder 520 components applied to a couple waveform pulses 610. Theprocessed waveform 660 has peaks A and C and corresponding valleys B andD. There are two edges, a first edge is represented by a line segment670 connecting A and B. A second edge is represented by a line segment680 connecting C and D.

As shown in FIG. 5, the candidate pulse portion also has delta T 530,zero crossing 540, amplitude threshold 550 and max-min 560 checks thateliminate certain of the identified edges. The delta T check 530discards all the edges having a distance between end points that do notfall within a fixed interval. This is designed to eliminate pulse-likeportions of the input waveform that are either too slow or too quick tobe physiological pulses. In a particular embodiment, the interval isbetween 5 and 30 samples at the 62.5 Hz sampling rate, or 80–480 msec.That is, edges less than 80 msec. or greater than 480 msec. in lengthare eliminated.

FIG. 7 illustrates the delta T check 530 (FIG. 5) described above. Shownis the processed waveform 760, edge a 780 and edge b 790, along with amaximum acceptable edge length interval 770 for comparison. In thisexample, edge a 780, which is 35 samples in length, would be eliminatedas exceeding in length the maximum acceptable interval 770 of 30samples. By contrast, edge b 790, which is 25 samples in length, wouldbe accepted.

Also shown in FIG. 5, the zero crossing check 540 eliminates all edgesthat do not cross zero. The zero crossing check eliminates smallcurvature changes in the input waveform segment, i.e. small bumps thatare not peaks and valleys.

FIG. 8 illustrates the effect of the zero crossing check 540 (FIG. 5).Shown is the processed waveform 860. Edge a 870, edge b 880 and edge c890 are shown relative to the zero line 865 for the processed waveform860. In this example, edges a 870 and edge b 880 are accepted, but edgec 890 is eliminated because it does not cross the zero line 865.

Shown in FIG. 5, the amplitude threshold check 550 is designed to removelarger “bumps” than the zero crossing check 540, such as dicroticnotches. This is done by comparing the right extreme (valley) of eachedge within a fixed-length window to a threshold based on a fixedpercentage of the minimum within that window. If the valley is notsufficiently deep, the edge is rejected. In a particular embodiment, thewindow size is set at 50 samples for neonates and 100 samples for adultsin order accommodate the slower pulse rate of an adult. Also, athreshold of 60% of the minimum is used.

FIG. 9 illustrates an example of the amplitude threshold check 550 (FIG.5). Shown is a the processed waveform 960. The starting point of thewindow 970 is set to the left extreme 942 (peak) of the first edge a. Aminimum 980 within the window 970 is determined. A threshold 982 equalto 60% of the minimum 980 is determined. The right extreme 992 of edge ais compared with the threshold 982. Edge a is kept because the rightextreme 992 is smaller than (more negative) than the threshold 982. Theright extreme 993 of edge b is then compared with the threshold 982.Edge b is removed because the right extreme 993 is greater than (lessnegative) than the threshold 982. Similarly, edge c is kept. Next, thewindow 970 is moved to the left extreme 943 of edge b and the processrepeated.

Also shown in FIG. 5, the max-min check 560 applies another removalcriteria to the edges. The max-min check 560 considers the interval ofthe processed waveform between the minimum of an edge being checked andthe peak of the subsequent edge. The max-min check 560 finds the maximumof the processed waveform within this interval. The edge being checkedis removed if the maximum is greater than a percentage of the absolutevalue of the right extreme (minimum) of that edge. In one embodimentrequiring the most stringent algorithm performance, the threshold is setto 77% of the right extreme of the edge. In another embodiment with lessstringent algorithm performance, the threshold is set to 200% of theright extreme of the edge. The max-min check 560 is effective ineliminating edges that are pulse-like but correspond to motion.

FIG. 10 illustrates an example of the max-min check 560 (FIG. 5). Shownis the processed waveform 1060. The max-min check 560 is applied to edgeb 1070. The interval B-C is considered, which is between point B 1074,the peak of edge b 1070, and point C 1084, the peak of edge c 1080. Themaximum in the interval B-C is point C 1084. Point C 1084 is compared toa first threshold 1078, which in this example is 77% of the absolutevalue of point P1 1072, the minimum of edge b 1070. Edge b 1070 wouldnot be discarded because point C 1084 is smaller than this firstthreshold 1078. As another example, the max-min check 560 is applied toedge c 1080. The interval C-D is considered, which is between point C1084, the peak of edge c 1080, and point D 1094, the peak of edge d1090. The maximum in the interval C-D is point V 1093. Point V 1093 iscompared to a second threshold 1088, which is 77% of the absolute valueof point P2 1082, the valley of edge c 1080. Edge c would be discardedbecause point V 1093 is greater than this second threshold 1088.

As shown in FIG. 5, the pulse finder 570 is the last component of thecandidate pulse subprocessor 410. The pulse finder 570 transforms theedges remaining after the various edge checks into candidate pulses inthe form of an idealized triangular wave, which are fed into theplethysmograph model subprocessor 460 (FIG. 4). From the informationabout the indices of the peaks of valleys of the remaining edges, it issimple to determine a pulse in the input waveform. The remaining edgesare first divided into edge pairs, i.e. the first and second edges, thesecond and third edges, and so on. The first point of a pulsecorresponds to the maximum of the waveform segment in the interval ofindices determined by the peak and valley of the first edge of a pair.The second point is the minimum between the valley of the first edge andthe peak of the second edge. The third and last point is the maximumbetween the peak and the valley of the second edge.

FIG. 11 illustrates the result of the pulse finder 570 (FIG. 5) shown asa series of pulses 1110, including a particular pulse XYZ 1120 appearingas a triangular wave superimposed on an input waveform segment 1140.Also shown are the remaining edges a 1170, b 1180 and c 1190. In thisexample, pulse XYZ 1120 is formed from the pair of edges c 1180 and e1190. Point X 1122 is the maximum in the waveform segment 1140 in thetime interval between the peak 1182 and valley 1184 of edge c 1180.Point Y 1124 is the minimum in the waveform segment 1140 in the timeinterval between the valley 1184 of edge c 1180 and the peak 1192 ofedge e 1190. Point Z 1128 is the maximum in the waveform segment 1140 inthe time interval between the peak 1192 and valley 1194 of edge e 1190.

FIG. 12 illustrates the components of the plethysmograph modelsubprocessor 460. This subprocessor takes as input the candidate pulsesidentified by the candidate pulse subprocessor 410 (FIG. 4) and decideswhich of these satisfies an internal model for a physiologicalplethysmographic waveform. Although the candidate pulse subprocessor 410(FIG. 4) performs a series of checks on edges, the plethysmograph modelsubprocessor performs a series of checks on pulse features. The firstcomponent of the model subprocessor calculates relevant pulse features.The remainder of the model subprocessor checks these pulse features toidentify physiologically acceptable features.

Shown in FIG. 12, the pulse features component 1210 extracts three itemsof information about the input candidate pulses that are needed fordownstream processing by the other components of the model subprocessor.The extracted features are the pulse starting point, period and signalstrength.

FIG. 13 illustrates a candidate pulse 1300 and the three parametersextracted by the pulse features component 1210 (FIG. 12). The pulse 1300is shown overlaid on the input waveform 1302 for reference. The startingpoint A 1360 is the first peak of the pulse 1300. The period P 1370 isthe time difference between the time of occurrence of the first peak1360 and the second peak 1362 of the pulse 1300. The signal strength SS1350 is the difference between the values of the first peak 1360 and thevalley 1364 of the pulse 1300. The signal strength SS 1350 is normalizedby dividing this value by the value of the infrared raw signal data atthe point corresponding to point A 1360.

Also shown in FIG. 12 is the 250 BPM check 1220. This component discardspulses having a period P 1370 (FIG. 13) that is below 15 samples. Thiscorresponds to an upper limit for the pulse rate set at 250 beats perminute. That is:15 samples/beat=(62.5 samples/sec.×60 sec./min.)/250beats per min  (2)

In addition, FIG. 12 shows the stick model check 1230. This componentdiscards pulses where the corresponding waveform does not closely fit astick model, i.e. where a pulse cannot be represented by a triangularwaveform. This component measures a normalized difference between theinput waveform and the triangular wave representation of that waveform.The obtained value is compared to a threshold, and pulses are discardedwhere the normalized difference is greater than that threshold.

FIG. 14 illustrates the calculations performed by the stick model check1230 (FIG. 12). Shown is an input waveform pulse 1410 and thecorresponding stick model pulse 1460. The stick model check 1230 (FIG.12) component computes a first value, which is a sum of the absolutedifferences, shown as the dark black areas 1420, between the waveformpulse 1410 and the stick model pulse 1460. This component also computesa second value, which is a sum of the first rectangular gray area 1470enclosing the descending portion of the pulse 1410 and the second grayarea 1480 enclosing the ascending portion of the pulse 1410. The stickmodel check 1230 (FIG. 12) then normalizes the first value by dividingit by the second value. This normalized value is compared with athreshold. A physiological pulse does not differ too much from the stickmodel at high pulse rates. This is not true at pulse rates much below150 bpm because of the appearance of a dicrotic notch and other “bumps.”Hence, the threshold is a function of pulse rate. In one embodiment, thethreshold is:0.15, for pulse rate<130  (3)0.430455769e^(−0.008109302)(pulse rate), for 130<pulse rate<160  (4)0.1, for pulse rate>160  (5)

Shown in FIG. 12 is the angle check 1240. The angle check 1240 is basedon computing the angle of a normalized slope for the ascending portionof a pulse. This angle is compared with the same angle of an ideal pulsehaving the same period. This check is effective in discarding pulsesthat are extremely asymmetric.

FIG. 15 illustrates an example of the angle check 1240 (FIG. 12). Shownis a single triangular pulse 1500 superimposed on the correspondinginput waveform 1502. The ascending pulse portion 1504 has a verticalrise a 1510 and a horizontal run b 1520. The rise 1510 and run 1520 arenormalized with respect to the pulse signal strength ss 1530 and thepulse frequency, which is 62.5 Hz. in this particular embodiment. Anangle θ 1540 is computed as:θ=arctan [(a/ss)/(b/62.5)]×180/π  (6)

The angle θ is compared with the same angle of an ideal pulse having thesame period, where a is equal to the signal strength and b is equal tothe period c 1550 minus 6. Three degrees are added to this value as athreshold margin. Hence, θ is compared to θ_(ref) computed as follow:θ_(ref)=arc tan {[a/ss]/[(c−6)/62.5]}×(180/π)+3  (7)

If θ<θ_(ref), then the pulse is discarded. FIG. 16 illustrates anexample pulse 1600 that would be discarded by the angle check, becausethe segment a 1610 is much smaller than the signal strength ss 1630.

Also shown in FIG. 12 is the ratio check 1250. The time ratio checkcomponent removes pulses in which the ratio between the duration of theascending pulse portion and the duration of the descending pulse portionis less than a certain threshold. In a particular embodiment, thethreshold is 1.1. The rationale for this check is that in everyphysiological pulse the ascending portion is shorter in time than thedescending portion, which represents the ventricular contraction.

FIG. 17 illustrates an example pulse 1700 that would be discarded by thetime ratio check 1250 (FIG. 12). In this example, the duration a 1760 ofthe ascending portion 1710 is less than the duration b 1770 of thedescending portion 1720. Hence, the ratio of the ascending duration 1760to the descending duration 1770, alb, is less than the threshold 1.1.

FIG. 12 further shows the signal strength check 1260. The signalstrength check 1260 assigns a confidence value to each pulse, based onits signal strength. There are two levels of confidence, high and low.The determination of confidence is based on two mechanisms. The firstmechanism is founded on the observation that the higher the pulse rate,the lower the signal strength. This mechanism is implemented with anempirical relationship between pulse rate and signal strength. If themeasured signal strength is greater than this empirical relationship bya fixed margin, the pulse confidence is low. The second mechanismincorporates the physiological limitation that signal strength cannotchange too much over a short period of time. If the pulse signalstrength is greater than a short-term average signal strength by a fixedmargin, the pulse confidence is low. If the pulse meets both criteria,then the pulse has a high confidence. All pulses in a single waveformsegment or snapshot have the same confidence value. Hence, if there is aleast one pulse with a high confidence, then all pulses with a lowconfidence will be dropped.

FIG. 18 illustrates the first signal strength criteria described above.In one embodiment, the relationship between signal strength and pulserate is given by curve 1800, which is described by the followingequation:SS=110.e^(−0.02131PR)+1  (8)

First, the pulse rate, PR 1810, is determined from the pulse period.Next, the corresponding signal strength, SS_(ref) 1820, is determinedfrom equation (8) and the pulse rate 1810. Because equation (8) isempirically derived, it is shifted up and down to make it moreapplicable for individual patients. A long-term average signal strength,Long Time SS 1830, and a long-term average pulse rate, Long Time PR1840, are derived. If Long Time SS 1830 is above the curve 1800 at thepoint corresponding to the Long Time PR 1840, then the differencebetween the Long Time SS and the curve 1800 plus 2 becomes Offset 1850.If the measured pulse signal strength, Pulse SS, is less thanSS_(ref)+Offset 1860, then this check is passed.

As shown in FIG. 4, after the candidate pulse subprocessor 410 and theplethysmograph model subprocessor 460, the pulse recognition processor400 has identified inside the input waveform snapshot all of the pulsesthat meet a certain model for physiologically acceptableplethysmographs. From the information about these pulses, the pulsestatistics subprocessor 490 can extract statistics regarding thesnapshot itself. Two useful statistical parameters that are derived arethe median value of the pulse periods and signal strengths. The medianis used rather than the mean because inside a waveform snapshot of 400points (almost 7 seconds) the period and signal strength associated witheach pulse can vary widely. Another parameter is the signal strengthconfidence level, which in one embodiment is the same for all therecognized pulses of a snapshot. A fourth useful parameter is pulsedensity. Pulse density is the value obtained by dividing the sum of theperiods of the acceptable pulses by the length of the snapshot. Pulsedensity represents that ratio of the snapshot that has been classifiedas physiologically acceptable. Pulse density is a value between 0 and 1,where 1 means that all of the snapshot is physiologically acceptable. Inother words, pulse density is a measure of whether the data is clean ordistorted, for example by motion artifact.

Finally, based on these described criteria, a pulse rate may be chosen.In a system with additional monitoring inputs, as depicted in FIG. 19, apulse rate selection and comparison module 1900 may be provided. Forexample, the oximeter pulse rate (and corresponding confidenceinformation if desired) can be provided on a first input 1902. In amultiparameter patient monitor, there may also be pulse rate or pulseinformation (and possibly confidence information) from an ECG or EKGmonitor on a second input 1904, from a blood pressure monitor on a thirdinput 1906, from an arterial line on a fourth input 1908, and otherpossible parameters 1910, 1912. The pulse rate module 1900 then comparesthe various inputs, and can determine which correlate or which correlateand have the highest confidence association. The selected pulse rate isthen provided on an output 1914. Alternatively, the pulse rate module1900 may average each input, a selection of the inputs or provide aweighted average based on confidence information if available.

The plethysmograph pulse recognition processor has been disclosed indetail in connection with various embodiments of the present invention.These embodiments are disclosed by way of examples only and are not tolimit the scope of the present invention, which is defined by the claimsthat follow. One of ordinary skill in the art will appreciate manyvariations and modifications within the scope of this invention.

1. A method for identifying pulses in a plethysmograph waveformcomprising: acquiring plethysmograph waveform data comprised of datarelated to one or more signals received from a light-sensitive detectorthat detects light having a plurality of wavelengths transmitted throughbody tissue carrying pulsing blood; removing at least some waveformfeatures from the plethysmograph waveform data that do not correspond toan idealized waveform: selecting data representing potential pulses fromwithin the plethysmograph waveform data; determining time domainfeatures of the data representing potential pulses; and utilizing saidtime domain features to identify which of the data representingpotential pulses corresponds to data representing physiologicallyacceptable pulses.
 2. The method of claim 1, wherein said determiningstep comprises determining at least one of a pulse starting point, aperiod, and a signal strength.
 3. The method of claim 2, wherein saidutilizing step comprises disregarding the data representing potentialpulses that have a period below a predetermined threshold.
 4. The methodof claim 2, wherein said utilizing step comprises disregarding ones ofthe data representing potential pulses that have a descending trend thatis generally slower that a subsequent ascending trend.
 5. The method ofclaim 2, wherein said utilizing step comprises disregarding ones of thedata representing potential pulses that are generally asymmetric.
 6. Themethod of claim 2, wherein said utilizing step comprises disregardingthe data representing potential pulses that have an ascending trend witha vertical rise that is smaller than the signal strength by apredetermined amount.
 7. The method of claim 2, wherein said utilizingstep comprises disregarding the data representing potential pulses forwhich a ratio between a duration of an ascending pulse portion and aduration of a descending pulse portion is less than a predeterminedthreshold.
 8. The method of claim 2, wherein said utilizing stepcomprises disregarding the data representing potential pulses that havea signal strength that differs from a short-term average signal strengthby greater than a predetermined amount.
 9. The method of claim 2,wherein said utilizing step comprises disregarding ones of said datarepresenting potential pulses that do not sufficiently comply with anempirical relationship between pulse rate and pulse signal strength. 10.The method of claim 1, wherein the idealized waveform comprises atriangular waveform.
 11. A device for monitoring physiologicalparameters of a patient, the device comprising a processor capable ofidentifying a plurality of potential pulses within a plethysmographwaveform comprised of one or more signals received from alight-sensitive detector that detects light having a plurality ofwavelengths transmitted through body tissue carrying pulsing blood,wherein said processor is also capable of removing at least somewaveform features do not correspond to an idealized waveform from theplethysmogaph waveform, and identifying a physiologically acceptablepulse from among said plurality of potential pulses by evaluatingdifferences between a segment of said plethysmograph waveformcorresponding to a given potential pulse and an approximation of saidsegment.
 12. The device of claim 10, wherein said processor is alsocapable of identifying edges within said plethysmograph waveform,wherein each of said edges comprises a first end point at a peak of saidwaveform and a second end point at a subsequent valley of said waveform.13. The device of claim 12, wherein said processor is also capable ofdisregarding ones of said edges having a distance between said first andsecond end points that does not fall within a predetermined range. 14.The device of claim 12, wherein said processor is also capable ofdisregarding ones of said edges that do not cross zero.
 15. The deviceof claim 12, wherein said processor is also capable of disregarding onesof said edges having a second end point that is not below a threshold.16. The device of claim 12, wherein said processor is also capable ofdetermining the maximum value of said waveform between the second endpoint of a first edge and the first end point of a second subsequentedge and disregards said first edge if said maximum value exceeds athreshold.
 17. The device of claim 11, wherein said processor is alsocapable of generating a triangular waveform with points corresponding topoints within said plethysmograph waveform.
 18. The device of claim 11,wherein said approximation of said plethysmograph waveform segmentcomprises a stick model of said segment.
 19. The device of claim 11,further comprising a multiparameter patient monitor.
 20. The device ofclaim 19, wherein said multiparameter patient monitor comprises a pulseoximeter.
 21. The device of claim 20, wherein said processor is alsocapable of receiving a plurality of inputs comprising potential pulserates and provides an output comprising a pulse rate, wherein at leastone of said plurality of inputs is received from said pulse oximeter.22. The device of claim 21, wherein said output pulse rate is selectedby comparing said inputs and evaluating correlations between saidinputs.
 23. The device of claim 21, wherein a confidence level isassociated with one or more of said inputs and said output pulse rate isselected by evaluating correlations between said inputs and theconfidence levels associated with said inputs.
 24. The device of claim21, wherein said output pulse rate comprises an average of two or moreof said inputs.
 25. The device of claim 21, wherein a confidence levelis associated with one or more of said inputs and said output pulse ratecomprises an average of two or more of said inputs that is weightedbased on the confidence levels associated with said inputs.
 26. Thedevice of claim 11, wherein the idealized waveform comprises atriangular waveform.